PKG Movement Recording System Use Shows Promise in Routine Clinical Care of Patients With Parkinson's Disease

Evaluation of the Parkinson's Remote Interactive Monitoring System in a Clinical Setting: Usability Study

BACKGROUND

The fastest-growing neurological disorder is Parkinson disease (PD), a progressive neurodegenerative disease that affects 10 million people worldwide. PD is typically treated with levodopa, an oral pill taken to increase dopamine levels, and other dopaminergic agonists. As the disease advances, the efficacy of the drug diminishes, necessitating adjustments in treatment dosage according to the patient's symptoms and disease progression. Therefore, remote monitoring systems that can provide more detailed and accurate information on a patient's condition regularly are a valuable tool for clinicians and patients to manage their medication. The Parkinson's Remote Interactive Monitoring System (PRIMS), developed by PragmaClin Research Inc, was designed on the premise that it will be an easy-to-use digital system that can accurately capture motor and nonmotor symptoms of PD remotely.

OBJECTIVE

We performed a usability evaluation in a simulated clinical environment to assess the ease of use of the PRIMS and determine whether the product offers suitable functionality for users in a clinical setting.

METHODS

Participants were recruited from a user sign-up web-based database owned by PragmaClin Research Inc. A total of 11 participants were included in the study based on the following criteria: (1) being diagnosed with PD and (2) not being diagnosed with dementia or any other comorbidities that would make it difficult to complete the PRIMS assessment safely and independently. Patient users completed a questionnaire that is based on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. Interviews and field notes were analyzed for underlying themes and topics.

RESULTS

In total, 11 people with PD participated in the study (female individuals: n=5, 45%; male individuals: n=6, 55%; age: mean 66.7, SD 7.77 years). Thematic analysis of the observer's notes revealed 6 central usability issues associated with the PRIMS. These were the following: (1) the automated voice prompts are confusing, (2) the small camera is problematic, (3) the motor test exhibits excessive sensitivity to the participant's orientation and position in relation to the cameras, (4) the system poses mobility challenges, (5) navigating the system is difficult, and (6) the motor test exhibits inconsistencies and technical issues. Thematic analysis of qualitative interview responses revealed four central themes associated with participants' perspectives and opinions on the PRIMS, which were (1) admiration of purpose, (2) excessive system sensitivity, (3) video instructions preferred, and (4) written instructions disliked. The average system usability score was calculated to be 69.2 (SD 4.92), which failed to meet the acceptable system usability score of 70.

CONCLUSIONS

Although multiple areas of improvement were identified, most of the participants showed an affinity for the overarching objective of the PRIMS. This feedback is being used to upgrade the current PRIMS so that it aligns more with patients' needs.

3D Kinematics Quantifies Gait Response to Levodopa earlier and to a more Comprehensive Extent than the MDS-Unified Parkinson's Disease Rating Scale in Patients with Motor Complications

BACKGROUND

Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies.

OBJECTIVES

This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold-standard MDS-UPDRS motor part III.

METHODS

Seventeen PD patients were assessed while wearing a full-body set of 15 IMUs in their OFF state and at 20-minute intervals following the administration of a supra-threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non-PD control subjects were conducted in parallel.

RESULTS

Significant motor changes were observed in both the upper and lower limbs according to the MDS-UPDRS III, 40 minutes after levodopa intake. IMU-assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace-domain" gait features showed significant improvement in the Best-ON state, most rhythmicity, asymmetry, and variability features did not.

CONCLUSION

IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS-UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions.

Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease

Background

Impaired motor and cognitive function can make travel cumbersome for People with Parkinson's disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed.

Objective

To explore the feasibility of remote clinic-to-clinic telehealth research visits for evaluation of multi-modal function in PwPD.

Methods

PwPD residing within 30 miles of a UAMS Regional health center were enrolled and clinic-to-clinic telehealth visits were performed. Motor and non-motor disease assessments were administered and quantified. Results were compared to participants who performed at-home telehealth visits using the same protocols during the height of the COVID pandemic.

Results

Compared to the at-home telehealth visit group (n = 50), the participants from regional centers (n = 13) had similar age and disease duration, but greater disease severity with higher total Unified Parkinson's disease rating scale scores (Z = -2.218, p = 0.027) and lower Montreal Cognitive Assessment scores (Z = -3.350, p < 0.001). Regional center participants had lower incomes (Pearson's chi = 21.3, p < 0.001), higher costs to attend visits (Pearson's chi = 16.1, p = 0.003), and lived in more socioeconomically disadvantaged neighborhoods (Z = -3.120, p = 0.002). Prior research participation was lower in the regional center group (Pearson's chi = 4.5, p = 0.034) but both groups indicated interest in future research participation.

Conclusions

Regional center research visits in PwPD in medically underserved areas are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

Improving naturalistic neuroscience with patient engagement strategies

Introduction

The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings.

Concepts and results

We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures.

Conclusion

We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.

Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson's Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson's Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.

Overview on wearable sensors for the management of Parkinson's disease

Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.

Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's

INTRODUCTION

In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP.

METHODS AND ANALYSIS

This single-centre, UK-based study, will recruit 55 participants with Parkinson's. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens.

ETHICS AND DISSEMINATION

Ethical approval was granted by London-142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent.

TRIAL REGISTRATION NUMBER

ISRCTN13156149.

Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor®

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor^®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.

Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data

BACKGROUND

Standardized screening for subthalamic deep brain stimulation (STN DBS) in Parkinson's disease (PD) patients is crucial to determine eligibility, but its utility to predict postoperative outcomes in eligible patients is inconclusive. It is unknown whether wearable data can contribute to this aim.

OBJECTIVE

To evaluate the utility of universal components incorporated in the DBS screening, complemented by a wearable sensor, to predict motor outcomes and Quality of life (QoL) one year after STN DBS surgery.

METHODS

Consecutive patients were included in the OPTIMIST cohort study from two DBS centers. Standardized assessments included a preoperative Levodopa Challenge Test (LCT), and questionnaires on QoL and non-motor symptoms including cognition, psychiatric symptoms, impulsiveness, autonomic symptoms, and sleeping problems. Moreover, an ambulatory wearable sensor (Parkinson Kinetigraph (PKG)) was used. Postoperative assessments were similar and also included a Stimulation Challenge Test to determine DBS effects on motor function.

RESULTS

Eighty-three patients were included (median (interquartile range) age 63 (56-68) years, 36% female). Med-OFF (Stim-OFF) motor severity deteriorated indicating disease progression, but patients significantly improved in terms of Med-ON (Stim-ON) motor function, motor fluctuations, QoL, and most non-motor domains. Motor outcomes were not predicted by preoperative tests, including covariates of either LCT or PKG. Postoperative QoL was predicted by better preoperative QoL, lower age, and more preoperative impulsiveness scores in multivariate models.

CONCLUSION

Data from the DBS screening including wearable data do not predict postoperative motor outcome at one year. Post-DBS QoL appears primarily driven by non-motor symptoms, rather than by motor improvement.

Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial

INTRODUCTION

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming.

METHODS

We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements.

RESULTS

We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months.

CONCLUSION

MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP).

Methods

Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback.

Results

Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score &gt; 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS.

Conclusion

This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.

Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease

BACKGROUND

Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments.

OBJECTIVES

To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use.

METHODS

We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not.

RESULTS

Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG.

CONCLUSIONS

It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.

The Dashboard Vitals of Parkinson's: Not to Be Missed Yet an Unmet Need

The vitals of Parkinson's disease (PD) address the often-ignored symptoms, which are considered either peripheral to the central core of motor symptoms of PD or secondary symptoms, which, nevertheless, have a key role in the quality of life (QoL) and wellness of people with Parkinson's (PwP) [...].

Feasibility of telemedicine research visits in people with Parkinson's disease residing in medically underserved areas

Introduction

Gait, balance, and cognitive impairment make travel cumbersome for People with Parkinson's disease (PwPD). About 75% of PwPD cared for at the University of Arkansas for Medical Sciences' Movement Disorders Clinic reside in medically underserved areas (MUAs). Validated remote evaluations could help improve their access to care. Our goal was to explore the feasibility of telemedicine research visits for the evaluation of multi-modal function in PwPD in a rural state.

Methods

In-home telemedicine research visits were performed in PwPD. Motor and non-motor disease features were evaluated and quantified by trained personnel, digital survey instruments for self-assessments, digital voice recordings, and scanned and digitized Archimedes spiral drawings. Participant's MUA residence was determined after evaluations were completed.

Results

Twenty of the fifty PwPD enrolled resided in MUAs. The groups were well matched for disease duration, modified motor UPDRS, and Montreal Cognitive assessment scores but MUA participants were younger. Ninety-two percent were satisfied with their visit, and 61% were more likely to participate in future telemedicine research. MUA participants traveled longer distances, with higher travel costs, lower income, and education level. While 50% of MUA participants reported self-reliance for in-person visits, 85% reported self-reliance for the telemedicine visit. We rated audio-video quality highly in approximately 60% of visits in both groups. There was good correlation with prior in-person research assessments in a subset of participants.

Conclusions

In-home research visits for PwPD in MUAs are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

Use of Laughter for the Detection of Parkinson's Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques

Parkinson's disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based on laugh analysis with speech recognition methods and automatic classification techniques. We evaluated different cepstral coefficients to identify laugh characteristics of healthy and ill subjects combined with machine learning classification models. The decision support system reached 83% accuracy rate with an AUC value of 0.86 for PD-healthy laughs classification in a database of 20,000 samples randomly generated from a pool of 120 laughs from healthy and PD subjects. Laughter could be employed for the efficient and reliable detection of PD; such a detection system can be achieved using speech recognition and automatic classification techniques; a clinical decision support system can be built using the above techniques. Significance: PD clinical decision support systems for the early detection of the disease will help to improve the efficiency of available and upcoming therapeutic treatments which, in turn, would improve life conditions of the affected people and would decrease costs and efforts in public and private healthcare systems.

Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring

BACKGROUND

Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined.

OBJECTIVE

To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC).

METHODS

Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC.

RESULTS

Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs).

CONCLUSION

In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies.

A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.

A Long Way to Go: Patient Perspectives on Digital Health for Parkinson's Disease

Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson’s disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis shows that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.

Cost-effectiveness analysis of the Parkinson's KinetiGraph and clinical assessment in the management of Parkinson's disease

AIMS

The Parkinson's KinetiGraph (PKG) is a wrist-worn movement recording system that collates continuous, objective, data during daily activities in people with Parkinson's disease (PD) providing a report for clinicians. This study explores the cost-effectiveness of adding the PKG to routine PD assessments.

METHODS

A de novo Markov model of three health states: uncontrolled, controlled and death compared PKG plus routine assessment by a Movement Disease Specialist (MDS) versus routine assessment. Uncontrolled and controlled states were based on the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) II and III scores. The transition between health states was dependent on improvement in MDS-UPDRS II and III, and transition to death state on all cause-mortality and PD-specific relative mortality risk. Markov cycle length was yearly beyond year 1 and lifetime horizon 22 years.

LIMITATIONS

PKG evidence incorporated in this analysis is based on findings from one clinical trial. Health state utilities were mapped and the probability of patients progressing from uncontrolled to controlled health state at the second visit and beyond was derived from a bootstrap method which assumed a normal distribution for MDS-UPDRS.

RESULTS

The addition of the PKG to usual PD assessments is a cost-effective intervention. PKG plus routine assessment is associated with lower total costs compared to routine assessment (£141,950 versus £159,312) and improved quality-adjusted life years (7.88 versus 7.61), resulting in an incremental cost-effectiveness ratio of -£64,978.99 and a net monetary benefit of £22,706.37 using a £20,000 threshold. Results were robust across sensitivity and scenario analyses.

CONCLUSIONS

Management of PD involves monitoring and evaluation of symptoms to assess disease progression and ensure appropriate treatment choices. Adding the PKG to clinical assessment in routine care allows for improved and objective identification of PD motor symptoms which can be used in clinical decision making to improve patient outcomes.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer

Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Sleep-Aware Adaptive Deep Brain Stimulation Control: Chronic Use at Home With Dual Independent Linear Discriminate Detectors

Adaptive deep brain stimulation (aDBS) is a promising new technology with increasing use in experimental trials to treat a diverse array of indications such as movement disorders (Parkinson’s disease, essential tremor), psychiatric disorders (depression, OCD), chronic pain and epilepsy. In many aDBS trials, a neural biomarker of interest is compared with a predefined threshold and stimulation amplitude is adjusted accordingly. Across indications and implant locations, potential biomarkers are greatly influenced by sleep. Successful chronic embedded adaptive detectors must incorporate a strategy to account for sleep, to avoid unwanted or unexpected algorithm behavior. Here, we show a dual algorithm design with two independent detectors, one used to track sleep state (wake/sleep) and the other used to track parkinsonian motor state (medication-induced fluctuations). Across six hemispheres (four patients) and 47 days, our detector successfully transitioned to sleep mode while patients were sleeping, and resumed motor state tracking when patients were awake. Designing “sleep aware” aDBS algorithms may prove crucial for deployment of clinically effective fully embedded aDBS algorithms.

Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review

BACKGROUND

The World Health Organisation's global strategy for digital health emphasises the importance of patient involvement. Understanding the usability and acceptability of wearable devices is a core component of this. However, usability assessments to date have focused predominantly on healthy adults. There is a need to understand the patient perspective of wearable devices in participants with chronic health conditions.

METHODS

A systematic review was conducted to identify any study design that included a usability assessment of wearable devices to measure mobility, through gait and physical activity, within five cohorts with chronic conditions (Parkinson's disease [PD], multiple sclerosis [MS], congestive heart failure, [CHF], chronic obstructive pulmonary disorder [COPD], and proximal femoral fracture [PFF]).

RESULTS

Thirty-seven studies were identified. Substantial heterogeneity in the quality of reporting, the methods used to assess usability, the devices used, and the aims of the studies precluded any meaningful comparisons. Questionnaires were used in the majority of studies (70.3%; n = 26) with a reliance on intervention specific measures (n = 16; 61.5%). For those who used interviews (n = 17; 45.9%), no topic guides were provided, while methods of analysis were not reported in over a third of studies (n = 6; 35.3%).

CONCLUSION

Usability of wearable devices is a poorly measured and reported variable in chronic health conditions. Although the heterogeneity in how these devices are implemented implies acceptance, the patient voice should not be assumed. In the absence of being able to make specific usability conclusions, the results of this review instead recommends that future research needs to: (1) Conduct usability assessments as standard, irrespective of the cohort under investigation or the type of study undertaken. (2) Adhere to basic reporting standards (e.g. COREQ) including the basic details of the study. Full copies of any questionnaires and interview guides should be supplied through supplemental files. (3) Utilise mixed methods research to gather a more comprehensive understanding of usability than either qualitative or quantitative research alone will provide. (4) Use previously validated questionnaires alongside any intervention specific measures.

Comparison of the Parkinson's KinetiGraph to off/on levodopa response testing: Single center experience

BACKGROUND AND OBJECTIVE

Levodopa off/on testing is frequently performed to assess medication response in patients with Parkinson's disease (PD) as an aid in determining best medical management or potential surgical candidacy. The Parkinson's Kinetigraph (PKG) is a wearable device which generates tremor, bradykinesia (BKS) and dyskinesia (DKS) scores representing motor symptoms over a six-day period. In this study, we compared off/on testing with PKG motor scores.

METHODS

Patients were enrolled as part of an observational study: Assessing the Longitudinal Signs in PD, a three-year study evaluating clinical and biomarker evolution in patients with PD taking levodopa. Patients underwent off/on testing at baseline and 6-month visits. A greater than 30% improvement between off and on MDS-Unified Parkinson's Disease Rating Scale scores was considered a robust response. After each visit, patients wore the PKG for 6 days. A bradykinesia score (BKS) greater than 26 and dyskinesia score (DKS) greater than 9 were considered poorly controlled bradykinesia and dyskinesia, respectively.

RESULTS

The median BKS at the baseline and 6-month visits were 27.15 and 27.55, respectively, despite a robust median off/on improvement at both visits. In addition, 10/18 (66%) and 7/13 (53.8%) patients with robust off/on improvement at the baseline and 6-month visits, respectively, demonstrated a BKS > 26 or DKS > 9.

CONCLUSION

A robust off/on response during a clinic visit does not necessarily reflect adequately controlled motor symptoms. The PKG, by virtue of its continuous recording of motor movements, may provide additional clinically relevant data on motor symptoms which may be useful for prospective observational studies.

Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial

Importance

The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy.

Objective

To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access.

Design, Settings, and Participants

This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain.

Interventions

The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care.

Main Outcomes and Measures

The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III.

Results

Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices.

Conclusions and Relevance

This study provides evidence supporting the safety and feasibility of postoperative home health DBS management.

Trial Registration

ClinicalTrials.gov Identifier: NCT02474459.

Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic

BACKGROUND

Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up.

CASE REPORT

We report two cases with PD, whose symptoms were monitored with a new wearable CE-marked system (PDMonitor^®), enabling appropriate treatment modifications.

CONCLUSION

Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.

Does Information from the Parkinson KinetiGraph™ (PKG) Influence the Neurologist's Treatment Decisions?-An Observational Study in Routine Clinical Care of People with Parkinson's Disease

Management of Parkinson's disease traditionally relies solely on clinical assessment. The PKG objectively measures affected persons' movements in daily life. The present study evaluated how often PKG data changed treatment decisions in routine clinical care and to what extent the clinical assessment and the PKG interpretation differed. PKG recordings were performed before routine visits. The neurologist first made a clinical assessment without reviewing the PKG. Signs and symptoms were recorded, and a treatment plan was documented. Afterward, the PKG was evaluated. Then, the neurologist decided whether to change the initial treatment plan or not. PKG review resulted in a change in the initial treatment plan in 21 of 66 participants (31.8%). The clinical assessment and the PKG review differed frequently, mainly regarding individual overall presence of motor problems (67%), profile of bradykinesia/wearing off (79%), dyskinesia (35%) and sleep (55%). PKG improved the dialogue with the participant in 88% of cases. PKG and clinical variables were stable when they were repeated after 3-6 months. In conclusion, PKG information changes treatment decisions in nearly a third of people with Parkinson's disease in routine care. Standard clinical assessment and PKG evaluation are often non-identical. Objective measurements in people living with Parkinson's disease can add therapeutically relevant information.

Digital Technology in Movement Disorders: Updates, Applications, and Challenges

Purpose of Review

Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson’s disease (PD) and Huntington’s disease.

Recent Findings

Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights.

Summary

Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease

OBJECTIVE

We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.

METHODS

Using OpenPose, a deep learning-based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning-based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating.

RESULTS

For resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797).

CONCLUSION

Machine learning-based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that machine learning-based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist.

Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson's disease

BACKGROUND

To develop an effective, patient-centred and sustainable service, we set up a virtual clinic (VC) for patients with Parkinson's disease, combining phone consultations and reports from wearable technology. The Parkinson's Kinetigraph (PKG) is a wrist-worn device providing objective motor assessment, generating a report used by clinicians to optimise medication regimens.

INTERVENTIONS

A pilot study of VC was designed using quality improvement methodology. For a VC appointment, patients were phoned by a clinician. After discussing symptoms and reviewing the PKG report, the clinician could decide on any medication changes or other interventions and relay this to the patient's general practitioner in a clinic letter. Patient feedback was gathered via questionnaires and data collected on the outcomes and timings of the consultations.

RESULTS

Over 12 clinics, 61 patients had VC appointments. Of questionnaire respondents, 89% were satisfied with VC (n=41). At VC, the clinician was able to make a treatment decision comparable to a face-to-face clinic in 79% of cases (n=48). Reasons appointments were deemed unsuccessful included issues with the PKG, speech or hearing problems and complex phase of disease. VC appointments, including administration time, last on average 22 min. This compares to 20 min face-to-face appointments but these do not include administration time.

CONCLUSIONS

We have demonstrated a safe and effective VC template. Most VC appointments are equivalent to face-to-face clinic in terms of treatment outcome. Success could be further improved by appropriate patient selection. Using VC is time saving and can result in releasing face-to-face appointment slots for those in urgent need or newly referred patients. Further cost analysis is required; the cost of the PKG alone is more expensive than a face-to-face appointment, but this does not take into account other value added, such as patient convenience and satisfaction, and reduced need for ambulance transport.

Comparing Objective and Subjective Measures of Parkinson's Disease Using the Parkinson's KinetiGraph

Background: Parkinson's disease (PD) is a neurodegenerative disease that can lead to impaired motor function and execution of activities of daily living (ADL). Since clinicians typically can only observe patients' symptoms during visits, prescribed medication schedules may not reflect the full range of symptoms experienced throughout the day. Therefore, objective tools are needed to provide comprehensive symptom data to optimize treatment. One such tool is the Parkinson's KinetiGraph^® (PKG), a wearable sensor that measures motor symptoms of Parkinson's disease.

Objective: To build a mathematical model to determine if PKG data measuring Parkinson's patients' motor symptoms can predict patients' ADL impairment.

Methods: Thirty-four patients with PD wore the PKG device for 6 days while performing their ADL. Patients' PKG scores for bradykinesia and dyskinesia, as well as their responses to a questionnaire asking if their ADL-level had been impacted by various motor symptoms, were used to build a multiple regression model predicting the patients' Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part II scores.

Results: Calculation of bradykinesia score response to medication showed that using a dosage response time of 30 min yielded a greater bradykinesia response than when the response time was set to 40, 50, 60, 70, 80, or 90 min. The overall multiple regression model predicting MDS-UPDRS part II score was significant (R² = 0.546, p < 0.001).

Conclusion: The PKG's ability to provide motor symptom data that correlates with clinical measures of ADL impairment suggests that it has strong potential as a tool for the assessment and management of Parkinson's disease motor symptoms.

More Sensitive Identification for Bradykinesia Compared to Tremors in Parkinson's Disease Based on Parkinson's KinetiGraph (PKG)

The effective management and therapies for Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph (PKG) is a wearable sensor system that can monitor the motion characteristics of PD objectively and continuously. This study was aimed to assess the correlations between PKG data and clinical scores of bradykinesia, rigidity, tremor, and fluctuation. It also aims to explore the application value of identifying early motor symptoms. An observational study of 100 PD patients wearing the PKG for ≥ 6 days was performed. It provides a series of data, such as the bradykinesia score (BKS), percent time tremor (PTT), dyskinesia score (DKS), and fluctuation and dyskinesia score (FDS). PKG data and UPDRS scores were analyzed, including UPDRS III total scores, UPDRS III-bradykinesia scores (UPDRS III-B: items 23-26, 31), UPDRS III-rigidity scores (UPDRS III-R: item 22), and scores from the Wearing-off Questionnaire (WOQ-9). This study shows that there was significant correlation between BKS and UPDRS III scores, including UPDRS III total scores, UPDRS III-B, and UPDRS III-R scores (r = 0.479-0.588, p ≤ 0.001), especially in the early-stage group (r = 0.682, p < 0.001). Furthermore, we found that BKS in patients with left-sided onset (33.57 ± 5.14, n = 37) is more serious than in patients with right-sided onset (29.87 ± 6.86, n = 26). Our findings support the feasibility of using the PKG to detect abnormal movements, especially bradykinesia in PD. It is suitable for the early detection, remote monitoring, and timely treatment of PD symptoms.